[show abstract][hide abstract] ABSTRACT: Spatially distributed chlorophyll content of urban vegetation provides an important indicator of a plant's health status, which might depend on the habitat quality of the specific urban environment. Recent advances in optical remote sensing led to improved methodologies to monitor vegetation properties. The hyperspectral index NAOC (Normalized Area Over reflectance Curve) is one of these new tools that can be used for mapping chlorophyll content. In this paper we present the work done to quantify vegetation chlorophyll content over the city of Valencia (Spain) based on chlorophyll measurements of four representative tree species: the London plane tree (Platanus x. acerifolia), the Canarian date palm (Phoenix canariensis), the European nettle tree (Celtis australis) and the white mulberry (Morus alba). Measurements were acquired during the summer of 2011, in a field campaign in which for 320 leaf samples, chlorophyll content was measured both in the laboratory and by using a SPAD-502 chlorophyll meter. Both methods were correlated (R2 > 0.86), using best fit power type functions. During the field campaign an aircraft with a CASI (Compact Airborne Spectral Imager) hyperspectral sensor onboard overflew the city obtaining imagery with a spatial resolution of ∼1 m suitable to identify individual urban trees. From the CASI data the NAOC index was calculated and linked with the laboratory chlorophyll content measurements. This led to a detailed chlorophyll content map with a RMSE of 15 μg cm−2. Chlorophyll map analysis at the individual crown level suggests the applicability to identify trees with lowered chlorophyll content due to a suboptimal habitat quality.
[show abstract][hide abstract] ABSTRACT: Biophysical parameters such as leaf chlorophyll content (LCC) and leaf area index (LAI) are standard vegetation products that can be retrieved from Earth observation imagery. This paper introduces a new machine learning regression algorithms (MLRAs) toolbox into the scientific Automated Radiative Transfer Models Operator (ARTMO) software package. ARTMO facilitates retrieval of biophysical parameters from remote observations in a MATLAB graphical user interface (GUI) environment. The MLRA toolbox enables analyzing the predictive power of various MLRAs in a semiautomatic and systematic manner, and applying a selected MLRA to multispectral or hyperspectral imagery for mapping applications. It contains both linear and nonlinear state-of-the-art regression algorithms, in particular linear feature extraction via principal component regression (PCR), partial least squares regression (PLSR), decision trees (DTs), neural networks (NNs), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The performance of multiple implemented regression strategies has been evaluated against the SPARC dataset (Barrax, Spain) and simulated Sentinel-2 (8 bands), CHRIS (62 bands) and HyMap (125 bands) observations. In general, nonlinear regression algorithms (NN, KRR, and GPR) outperformed linear techniques (PCR and PLSR) in terms of accuracy, bias, and robustness. Most robust results along gradients of training/validation partitioning and noise variance were obtained by KRR while GPR delivered most accurate estimations. We applied a GPR model to a hyperspectral HyMap flightline to map LCC and LAI. We exploited the associated uncertainty intervals to gain insight in the per-pixel performance of the model.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 01/2014; · 2.87 Impact Factor
[show abstract][hide abstract] ABSTRACT: The main objective of FLEX is the measurement of vegetation chlorophyll fluorescence (Fs) from space and the exploitation of this signal to better understand the carbon cycle. FLuORescence Imaging Spectrometer (FLORIS) is the main instrument of the FLEX mission concept. ESA's Earth Science Advisory Committee recommended the investigation of the FLEX concept as an in-orbit demonstrator to be flown as a tandem mission with Sentinel-3 (S-3). S-3 is amongst others equipped with the Ocean Land Colour Instrument (OLCI). When flown in tandem these instruments are expected to provide an accurate characterization of key atmospheric and surface parameters to facilitate Fs retrieval for FLORIS. In this work the performance of FLORIS and S3-OLCI sensors and their synergy was evaluated on their capability of retrieving relevant biophysical parameters using simulated top-of-atmosphere radiance data (LTOA). For both sensors, LTOA data were simulated across a wide range of vegetation, atmospheric and geometry parameters by coupling leaf, canopy and atmospheric radiative transfer models. The pursued analysis was to train for each retrievable parameter (here: Chl, LAI, soil type and Ftotal) a regression model using the simulated datasets and then evaluate its performance. Two regression types were chosen, a conventional linear regressor and a more advanced nonlinear regressor, and two types of training/validation strategies were followed: a local strategy (at least 2 parameters fixed) and a generic strategy (uniform random subset of the complete dataset). The simulation study led to the following conclusions: 1) FLORIS is well equipped for accurate retrieval of biophysical parameters; 2) however, advanced nonlinear regressors may be needed to achieve robust results, and 3) the large number of bands can lead to redundancy in the nonlinear regressors which can be overcomed by band optimization strategies. Finally, 4) it was demonstrated that a synergy- of both FLORIS and S3-OLCI datasets leads to improved biophysical parameter retrieval.
[show abstract][hide abstract] ABSTRACT: Radiative transfer (RT) modeling plays a key role for earth observation
(EO) because it is needed to design EO instruments and to develop and
test inversion algorithms. The inversion of a RT model is considered as
a successful approach for the retrieval of biophysical parameters
because of being physically-based and generally applicable. However, to
the broader community this approach is considered as laborious because
of its many processing steps and expert knowledge is required to realize
precise model parameterization. We have recently developed a radiative
transfer toolbox ARTMO (Automated Radiative Transfer Models Operator)
with the purpose of providing in a graphical user interface (GUI)
essential models and tools required for terrestrial EO applications such
as model inversion. In short, the toolbox allows the user: i) to choose
between various plant leaf and canopy RT models (e.g. models from the
PROSPECT and SAIL family, FLIGHT), ii) to choose between spectral band
settings of various air- and space-borne sensors or defining own sensor
settings, iii) to simulate a massive amount of spectra based on a look
up table (LUT) approach and storing it in a relational database, iv) to
plot spectra of multiple models and compare them with measured spectra,
and finally, v) to run model inversion against optical imagery given
several cost options and accuracy estimates. In this work ARTMO was used
to tackle some well-known problems related to model inversion. According
to Hadamard conditions, mathematical models of physical phenomena are
mathematically invertible if the solution of the inverse problem to be
solved exists, is unique and depends continuously on data. This
assumption is not always met because of the large number of unknowns and
different strategies have been proposed to overcome this problem.
Several of these strategies have been implemented in ARTMO and were here
analyzed to optimize the inversion performance. Data came from the
SPARC-2003 dataset, which was acquired on the agricultural test site
Barrax, Spain. LUTs were created using the 4SAIL and FLIGHT models and
were inverted against CHRIS data in order to retrieve maps of
chlorophyll content (chl) and leaf area index (LAI). The following
inversion steps have been optimized: 1. Cost function. The performances
of about 50 different cost functions (i.e. minimum distance functions)
were compared. Remarkably, in none of the studied cases the widely used
root mean square error (RMSE) led to most accurate results. Depending on
the retrieved parameter, more successful functions were: 'Sharma and
Mittal', 'Shannońs entropy', 'Hellinger distance',
'Pearsońs chi-square'. 2. Gaussian noise. Earth observation data
typically encompass a certain degree of noise due to errors related to
radiometric and geometric processing. In all cases, adding 5% Gaussian
noise to the simulated spectra led to more accurate retrievals as
compared to without noise. 3. Average of multiple best solutions.
Because multiple parameter combinations may lead to the same spectra, a
way to overcome this problem is not searching for the top best match but
for a percentage of best matches. Optimized retrievals were encountered
when including an average of 7% (Chl) to 10% (LAI) top best matches.
4. Integration of estimates. The option is provided to integrate
estimates of biochemical contents at the canopy level (e.g., total
chlorophyll: Chl × LAI, or water: Cw × LAI), which can lead
to increased robustness and accuracy. 5. Class-based inversion. This
option is probably ARTMÓs most powerful feature as it allows
model parameterization depending on the imagés land cover classes
(e.g. different soil or vegetation types). Class-based inversion can
lead to considerably improved accuracies compared to one generic class.
Results suggest that 4SAIL and FLIGHT performed alike for Chl but not
for LAI. While both models rely on the leaf model PROSPECT for Chl
retrieval, their different nature (e.g. numerical vs. ray tracing) may
cause that retrieval of structural parameters such as LAI differ.
Finally, it should be noted that the whole analysis can be intuitively
performed by the toolbox. ARTMO is freely available to the EO community
for further development. Expressions of interest are welcome and should
be directed to the corresponding author.
[show abstract][hide abstract] ABSTRACT: The Fraunhofer Line Discriminator (FLD) principle has long been considered as the reference method to quantify solar-induced chlorophyll fluorescence (F) from passive remote sensing measurements. Recently, alternative retrieval algorithms based on the spectral fitting of hyperspectral radiance observations, Spectral Fitting Methods (SFMs), have been proposed. The aim of this manuscript is to investigate the performance of such algorithms and to provide relevant information regarding their use. FLD and SFMs were used to estimate F starting from Top Of Canopy (TOC) fluxes at very high spectral resolution (0.12 nm) and sampling interval (0.1 nm), exploiting the O2-B (687.0 nm) and O2-A (760.6 nm) atmospheric oxygen absorption bands overlapping the fluorescence emissions at the red and far-red spectral window.Specific parameters affecting FLD and SFM performances are investigated and the accuracy of F estimation of the two methods is compared. The problem related to the lack of independent measurements of F at canopy level, which prevents the direct assessment of F estimation accuracy with actual measurements, is overcome in this study by using a modeled database of TOC reflectance spectra. In order to compute accuracy figures valid for operative applications the simulated spectra were perturbed by the addition of radiometric noise.An investigation was conducted to determine the best FLD channel configuration; it showed that violation of FLD assumptions results in a positive bias in F estimation at both oxygen absorption bands that cannot be avoided even at the high spectral resolution considered. SFMs were shown to be more accurate than FLD under any noise configuration considered.
Remote Sensing of Environment 03/2010; 114(2):363-374. · 5.10 Impact Factor
[show abstract][hide abstract] ABSTRACT: Solar-induced chlorophyll fluorescence is a weak electromagnetic signal emitted in the red and far-red spectral regions by vegetation chlorophyll under excitation by solar radiation. Chlorophyll fluorescence has been demonstrated to be a close proxy to vegetation physiological functioning. The basis for fluorescence retrieval from passive space measurements is the exploitation of the O2-A and O2-B atmospheric absorption features to isolate the fluorescence signal from the solar radiation reflected by the surface and the atmosphere. High spectral resolution measurements and a precise modeling of the atmospheric radiative transfer in the visible and near-infrared regions are mandatory. Recent developments for fluorescence retrieval from passive high spectral resolution spaceborne measurements are presented in this work, which has been performed in preparation of the FLuorescence EXplorer (FLEX) mission, which is currently under development by the European Space Agency. A large data set of FLEX-like measurements has been simulated for the purpose of methodology development and testing. Issues related to vegetation chlorophyll fluorescence retrieval from space, a description of the proposed methodology, initial results from simulated test cases, and general guidelines for the specification of fluorescence retrieval instruments are presented and discussed in this work.
Journal of Geophysical Research 01/2010; 115. · 3.17 Impact Factor
[show abstract][hide abstract] ABSTRACT: The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.
Hydrology and Earth System Sciences 01/2009; · 3.59 Impact Factor
[show abstract][hide abstract] ABSTRACT: The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence.
[show abstract][hide abstract] ABSTRACT: Interest in remote sensing (RS) of solar-induced chlorophyll fluorescence (F) by terrestrial vegetation is motivated by the link of F to photosynthetic efficiency which could be exploited for large scale monitoring of plant status and functioning. Today, passive RS of F is feasible with different prototypes and commercial ground-based, airborne, and even spaceborne instruments under certain conditions. This interest is generating an increasing number of research projects linking F and RS, such as the development of new F remote retrieval techniques, the understanding of the link between the F signal and vegetation physiology and the feasibility of a satellite mission specifically designed for F monitoring. This paper reviews the main issues to be addressed for estimating F from RS observations. Scattered information about F estimation exists in the literature. Here, more than 40 scientific papers dealing with F estimation are reviewed and major differences are found in approaches, instruments and experimental setups. Different approaches are grouped into major categories according to RS data requirements (i.e. radiance or reflectance, multispectral or hyperspectral) and techniques used to extract F from the remote signal. Theoretical assumptions. advantages and drawbacks of each method are outlined and provide perspectives for future research. Finally, applications of the measured F signal at the three scales of observation (ground, aircraft and satellite) are presented and discussed to provide the state of the art in F estimation. (C) 2009 Elsevier Inc. All rights reserved.
REMOTE SENSING OF ENVIRONMENT. 01/2009; 113(10):2037-2051.
[show abstract][hide abstract] ABSTRACT: The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de ecnica Aerospacial (INTA), and it has been involved in several field campaigns 5 since 2004. This paper presents an overview of the work performed with the AHS thermal im-agery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms 10 for land surface temperature and emissivity retrieval as well as the estimation of evap-otranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.